Developing a Socio-Technical Governance Framework for AI-Driven Mental Health Monitoring in Higher Education and Digital Behavioral Care Networks (The Science Post, 2026)
The rising prevalence of student mental health crises in higher education has outpaced traditional counseling resources, prompting the exploration of AI-driven monitoring systems that analyze digital behavioral data (e.g., LMS activity, communication patterns). However, existing approaches lack validated governance frameworks that balance predictive accuracy with ethical safeguards, creating a critical research gap. This study addresses this gap by developing and testing a novel Socio-Technical Governance Framework (STGF) for algorithmic vigilance. Using a design-based research methodology, we integrated retrospective digital exhaust data (n=2,450 students) with prospective agent-based simulations across three university settings. Key findings demonstrate that a hybrid random forest-LSTM model achieves 89.4% accuracy (F1=0.87, AUC=0.92) in predicting moderate-to-severe distress episodes 14–21 days in advance, significantly outperforming baseline methods (p